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Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

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Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach remote sensing Article Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach Guanghui Qi 1,2, Chunyan Chang 2, Wei Yang 3, Peng Gao 2 and Gengxing Zhao 2,* 1 College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China; [email protected] 2 National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China; [email protected] (C.C.); [email protected] (P.G.) 3 The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin’an, Hangzhou 311300, China; [email protected] * Correspondence: [email protected]; Tel.: +86-05388243939 Abstract: Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partial least squares inversion model was constructed by the fused UAV image. Then, inversion model was scaled up to the satellite by the TsHARP method, and finally, the accuracy of the satellite- Citation: Qi, G.; Chang, C.; Yang, W.; UAV-ground inversion model and results was verified. The results show that the band fusion of Gao, P.; Zhao, G. Soil Salinity UAV and ground images effectively enrich the spectral information of the UAV image. The accuracy Inversion in Coastal Corn Planting of the inversion model constructed based on the fused UAV images was improved. The inversion Areas by the Satellite-UAV-Ground results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with Integration Approach. Remote Sens. the measured soil salinity (R2 = 0.716 and RMSE = 0.727), and the inversion model had excellent 2021, 13, 3100. https://doi.org/ universal applicability. This research integrated the advantages of multi-source data to establish a 10.3390/rs13163100 unified satellite-UAV-ground model, which improved the ability of large-scale remote sensing data Academic Editors: Ephrem to finely indicate soil salinity. Habyarimana and Nicolas Greggio Keywords: Sentinel-2A; UAV; ground imaging hyperspectral; multi-source remote sensing data; Received: 30 June 2021 soil salinity Accepted: 3 August 2021 Published: 5 August 2021 Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in Globally, declining soil quality poses a significant challenge to improving agricultural published maps and institutional affil- productivity, economic growth and a healthy environment [1,2]. In coastal areas, soil iations. salinity and alkalinity are major soil limiting factors for agricultural and land degrada- tion [3]. Soil salinization not only reduces soil quality and land productivity, leading to a decline in crop yield, but also threatens ecological security and sustainable land use [4–6]. With the change of natural environment and the disturbance of human behavior, regional Copyright: © 2021 by the authors. salinization becomes more and more serious, which affects the sustainable development Licensee MDPI, Basel, Switzerland. of agriculture coastal areas to a great extent [7]. It is of great significance for agricultural This article is an open access article production and sustainable development to accurately extract the soil salinization status distributed under the terms and and grasp its spatial distribution law in the main crop corn planting area of coastal areas. conditions of the Creative Commons To improve the accuracy and efficiency of obtaining regional soil salinization spa- Attribution (CC BY) license (https:// tial distribution information is a prerequisite for rational management and utilization creativecommons.org/licenses/by/ of salinized soil [8]. The traditional method of obtaining soil salinity information in the 4.0/). Remote Sens. 2021, 13, 3100. https://doi.org/10.3390/rs13163100 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 3100 2 of 17 corn planting area is mainly through field survey sampling and laboratory testing, which is time consuming and laborious. Remote sensing technology has become a frequently used method for the quantitative analysis of soil salinization information because of its advantages of fast, large-scale, and non-destructive acquisition of ground feature infor- mation [9]. At present, ground, UAV and satellite remote sensing technologies provide a powerful means for monitoring the salt content of surface soils and have been widely used in the monitoring of farmland soil salinization [10–13]. Mohammad et al. [14] implemented the monitoring of soil salinity in a large area of Qom County, Qom Province, Iran, based on Sentinel-2A data. Wei et al. [15] took advantage of UAV equipped with Micro-MCA multispectral sensors to obtain images and realized the estimation of soil salinity in a small area of Hetao Irrigation District. Wang et al. [16] utilized a portable spectrometer ASD to obtain soil hyperspectral to construct a soil salinity inversion model in Baidunzi Basin, China, and achieved high model accuracy. However, most satellite images have low spatial resolution, so it is hard to achieve high precision real-time monitoring; UAV technology can obtain images with high temporal and spatial resolution, but the observation range is small, and it is unable to realize large scale monitoring; hyperspectral data can build high precision inversion models, but point information is incapable of monitoring soil salinity in a continuous spatial range. Therefore, due to the mutual constraints between the spatial resolution, spectral resolution and imaging width of the sensor, it is difficult for a single sensor to meet the requirements of large-scale, high precision and rapid soil salinity monitoring simultaneously [17]. Making full use of the complementary advantages of multi-source remote sensing data to carry out satellite-UAV-ground integrated inversion is an possible way to improve the accuracy of remote sensing inversion of regional soil salinity [18–20]. At present, Satellite-UAV-ground multi-source optical remote sensing data fusion has been applied to regional soil salinity inversion [21–25]. Solmaz et al. [26] constructed an in- version model through Landsat 8 and ASTER image fusion to obtain the spatial distribution of soil salinity in the Balikhli-Chay watershed, which improved the accuracy of soil salinity monitoring in the watershed. However, by the affection of inconsistency of sensor bands and satellite data acquisition time, the accuracy of soil salinity inversion after data fusion still needed to be improved. Zhang et al. [27] took advantage of band correction coefficients to normalize the reflectivity of satellite images based on the correlation between UAV and satellite image reflectivity, which improved the accuracy of soil salinity inversion, but the relationship between UAV and satellite image band spectrum information is nonlinear, and it was unable to accurately express the relationship between the two using only normalized coefficients. Sun et al. [28] used the measured soil hyperspectral data and Landsat-8 OLI multispectral data fusion to improve the retrieval accuracy of soil salt, and analyzed the differences of salt remote sensing in different seasons. Jia et al. [29] built a soil salinization estimation model based on the fusion of ASD hyperspectral and Landsat-8 OLI images, which expanded hyperspectral data from isolated point information to pixel and regional scale; however, due to the large spectral differences, it was difficult to effectively establish the corresponding relationship between the two samples, limiting the improvement of the inversion accuracy. In conclusion, the satellite-UAV-ground integration of regional soil salinity inversion is subject to the following limitations at present. First of all, the discrete hyperspectral observation data cannot accurately match the continuous spatial scale of UAV and satellite data; second, the simple linear method is used to fuse remote sensing data of different scales, and the accuracy of the inversion model built therefrom is limited; third, until now most of the inversion research based on satellite-UAV-ground multi-source remote sensing data fusion is based on the data fusion of two platforms, and the research of soil salinity inversion systems based on the integration of remote sensing data of three platforms still needs further exploration. The overall objective of this research is to to explore the nonlinear fusion method of ground imaging hyperspectral and UAV multi-spectral images, build a soil salinity inversion model based on the fused UAV images, and then to try to construct a high Remote Sens. 2021, 13, 3100 3 of 18 the research of soil salinity inversion systems based on the integration of remote sensing data of three platforms still needs further exploration. The overall objective of this research is to to explore the nonlinear fusion method of ground imaging hyperspectral and UAV multi-spectral images, build a soil salinity
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